Dmytro Lituiev
- Plant Science top 5%
- Molecular Biology top 10%
- Artificial Intelligence top 5%
- Radiology, Nuclear Medicine and Imaging top 10%
- Neurology top 10%
- Co-authors
- Ueli GrossniklausChristina Maria FranckAurélien Boisson‐DernierBruno MüllerPaul T. TarrHannes VoglerQuy A. NgoDexter Hadley
- Topics
- Plant Molecular Biology Research (7 papers)Plant Reproductive Biology (7 papers)Radiomics and Machine Learning in Medical Imaging (4 papers)
- Partner nations
- United StatesSwitzerlandUkraine
In The Last Decade
Dmytro Lituiev
20 papers receiving 1.5k citations
Hit Papers
Peers
Comparison fields: 5 of 128
- Plant Science 796
- Molecular Biology 738
- Artificial Intelligence 227
- Radiology, Nuclear Medicine and Imaging 205
- Neurology 111
Countries citing papers authored by Dmytro Lituiev
This map shows the geographic impact of Dmytro Lituiev's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Dmytro Lituiev with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dmytro Lituiev more than expected).
Fields of papers citing papers by Dmytro Lituiev
This network shows the impact of papers produced by Dmytro Lituiev. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Dmytro Lituiev. The network helps show where Dmytro Lituiev may publish in the future.
Co-authorship network of co-authors of Dmytro Lituiev
This figure shows the co-authorship network connecting the top 25 collaborators of Dmytro Lituiev. A scholar is included among the top collaborators of Dmytro Lituiev based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Dmytro Lituiev. Dmytro Lituiev is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 21 | |
| 2 | 0 | |
| 3 | 14 | |
| 4 | 0 | |
| 5 | 4 | |
| 6 | 15 | |
| 7 | 11 | |
| 8 | 5 | |
| 9 | 11 | |
| 10 | 145 | |
| 11 | 17 | |
| 12 | A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brainbreakdown → | 379 |
| 13 | 45 | |
| 14 | 155 | |
| 15 | 5 | |
| 16 | 223 | |
| 17 | 57 | |
| 18 | 260 | |
| 19 | 7 | |
| 20 | 6 |
About Dmytro Lituiev
Dmytro Lituiev is a scholar working on Health Informatics, Nephrology and Radiology, Nuclear Medicine and Imaging, having authored 22 papers that have together received 1.5k indexed citations. Recurring topics across this work include Plant Molecular Biology Research (7 papers), Plant Reproductive Biology (7 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). The work is most often cited by research in Health Informatics (94 citations), Plant Science (796 citations) and Health Information Management (77 citations). Dmytro Lituiev has collaborated with scholars based in United States, Switzerland and Ukraine. Frequent co-authors include Ueli Grossniklaus, Christina Maria Franck, Aurélien Boisson‐Dernier, Bruno Müller, Paul T. Tarr, Hannes Vogler, Quy A. Ngo, Dexter Hadley, Benjamin L. Franc and Atul J. Butte. Their work appears in journals such as Proceedings of the National Academy of Sciences, The Plant Cell and Development.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.